From Readership to Usership: Communicating Heritage Digitally Through Presence, Embodiment and Aesthetic Experience
Bibliographic record
Abstract
The primary mission of cultural institutions, including heritage sites and museums, is to perform and perpetuate Cultural Heritage (CH) by ideally transforming audiences into stewards of that heritage. In recent years, these institutions have increasingly turned to Mixed Reality (MR) technologies to expand and democratize public access to Cultural Heritage—a trend that is called upon to accelerate with COVID-19—because these technologies provide opportunities for more remote outreach, and moreover, can make partial remains or ruins more relatable to the public. But as emerging evaluations indicate, existing MR intangible and tangible Digital Cultural Heritage (DCH) applications are largely proving inadequate to engaging audiences beyond an initial fascination with the immersive 3D visualization of heritage sites and artefacts owing in part to misguided storytelling or non-compelling narratives. They fail to effectively communicate the significance of Cultural Heritage to audiences and impress upon them its value in a lasting way due to their overreliance on an education-entertainment-touristic consumption paradigm. Building on the recent case made for Literature-based MR Presence, this article examines how the literary tradition of travel narratives can be recruited to enhance presence and embodiment, and further elicit aesthetic experiences in Digital Cultural Heritage applications by drawing on recent findings from the fields of Extended Reality (XR), cognitive literary science and new museology. The projected effects of this innovative approach are not limited to an increase in audience engagement on account of a greater sense of presence and embodiment. This approach is also expected to prompt a different kind of public involvement characterized by a personal valuation of the heritage owing to aesthetic experience. As the paper ultimately discusses, this response is more compatible both with MR applications’ default mode of usership, and with newly emerging conceptions of a user-centered museum (e.g., the Museum 3.0), thereby providing a narrative roadmap for future Virtual Museum (VM) applications better suited to the primary mission of transmitting and perpetuating Cultural Heritage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".